SplitFusion enables ultrasensitive gene fusion detection and reveals fusion variant-associated tumor heterogeneity
Gene fusions are common cancer drivers and therapeutic targets, but clinical-grade open-source bioinformatic tools are lacking. Here, we introduce a fusion detection method named SplitFusion, which is fast by leveraging Burrows-Wheeler Aligner-maximal exact match (BWA-MEM) split alignments, can detect cryptic splice-site fusions (e.g., <i>EML4::ALK</i> v3b and <i>ARv7</i>), call fusions involving highly repetitive gene partners (e.g., <i>CIC::DUX4</i>), and infer frame-ness and exon-boundary alignments for functional prediction and minimizing false positives. Using 1,848 datasets of various sizes, SplitFusion demonstrated superior sensitivity and specificity compared to three other tools. In 1,076 formalin-fixed paraffin-embedded lung cancer samples, SplitFusion identified novel fusions and revealed that <i>EML4::ALK</i> variant 3 was associated with multiple fusion variants coexisting in the same tumor. Additionally, SplitFusion can call targeted splicing variants. Using data from 515 The Cancer Genome Atlas (TCGA) samples, SplitFusion showed the highest sensitivity and uncovered two cases of <i>SLC34A2::ROS1</i> that were missed in previous studies. These capabilities make SplitFusion highly suitable for clinical applications and the study of fusion-defined tumor heterogeneity.
Authors
- Gad A. Getz (ORCID: https://orcid.org/0000-0002-0936-0753)
- Hongxian Wang (ORCID: https://orcid.org/0000-0001-6404-6137)
- Chenyu Lu (ORCID: https://orcid.org/0000-0002-9908-6099)
- Zongli Zheng (ORCID: https://orcid.org/0000-0003-4849-4903)
- Baifeng Zhang (ORCID: https://orcid.org/0000-0003-1966-2285)
- Siyu Bao (ORCID: https://orcid.org/0000-0002-6703-5393)
- Wah Cheuk (ORCID: https://orcid.org/0000-0003-3160-9591)
- William Chi Shing Cho (ORCID: https://orcid.org/0000-0003-4174-4586)
- Yee Man Chan
- Chloe Yufan Bao
- Binyamin A. Knisbacher (ORCID: https://orcid.org/0000-0002-4962-9956)
- Zhengbo Song (ORCID: https://orcid.org/0000-0002-4817-7775)
- Hoi Yee Keung
- Athena H. Y. Chu
- Valentina Nardi (ORCID: https://orcid.org/0000-0002-0486-416X)
- Mengsu Yang (ORCID: https://orcid.org/0000-0003-2083-2296)
- Jian Wang (ORCID: https://orcid.org/0000-0003-2291-640X)
- Wenxian Wang (ORCID: https://orcid.org/0000-0002-3047-4149)
- Yiping Zhang (ORCID: https://orcid.org/0000-0003-4425-2165)
- Juxiang Chen (ORCID: https://orcid.org/0000-0002-5171-9707)
- Zhenyu Gong (ORCID: https://orcid.org/0000-0002-7376-4816)
- C. Xu (ORCID: https://orcid.org/0000-0002-9021-6731)
- Weiwei Bian (ORCID: https://orcid.org/0000-0003-1536-2947)
- Zi-Ying Maggie Chow
Institutions
- Broad Institute (US)
- Harvard University (US)
- Bar-Ilan University (IL)
- City University of Hong Kong (HK)
- Second Military Medical University (CN)
- Chinese Academy of Sciences (CN)
- Karolinska Institutet (SE)
- Massachusetts General Hospital (US)
- Zhejiang Cancer Hospital (CN)
- City University of Hong Kong, Shenzhen Research Institute (CN)
- International Peace Maternity & Child Health Hospital (CN)
- Changhai Hospital (CN)
- Ming Wai Lau Centre for Reparative Medicine (HK)
- Fujian Provincial Cancer Hospital (CN)
- Center for Cancer Research (US)
- Queen Elizabeth Hospital (CN)
Publication Details
- Journal
- Patterns
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1016/j.patter.2025.101174
- Citations
- 2
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- National Natural Science Foundation of China
- Vetenskapsrådet
- China Scholarship Council
- Medical and Health Research Project of Zhejiang Province